Fast Rule-Based Prediction of Data Streams Using Associative Classification Mining

Abstract

Associative Classification is a recent and rewarding approach which combines associative rule mining and classification. This technique has attracted many researchers as it derives accurate classifier with effective rules. Associative classifiers are useful for application where maximum predictive accuracy is desired. Increasing access to huge datasets and corresponding demands to analyze these data has led to the development of new online algorithms for performing machine learning on data streams. There is a need to develop a decision support system for predicting the huge datasets generated by various applications in IT industry. In this paper we proposed two efficient techniques called PSTMiner and PSToSWMine for prediction of data streams. This research uses associative classification which builds a classifier with prediction rules of high interestingness values. Experimental results show that the performance of these two algorithms is highly competitive in terms of accuracy and performance time.